DOI: 10.5937/jaes16-18435
This is an open access article distributed under the CC BY-NC-ND 4.0 terms and conditions.
Volume 16 article 546 pages: 404 - 409
The
prerequisites for reducing the test sample chi-square Pearson test size from
400 to 32 or fewer examples while maintaining its power are considered. The
urgency of the problem results from the fact that when learning and testing the
biometric identification means to identify the personality, it is not possible
to use large volumes of learning and test samples. The conditions under which
the chi-square test on small samples from the continuous distribution of values
becomes a discrete distribution of values are formalized. Normal and uniform
laws of values distribution use histograms with uniform intervals, which
accurately relate the central intervals of the histogram to the mathematical
expectation calculated on the test sample. 16 experiments shown that the
chi-square-synchronized test built on histograms with four equal intervals has
a discrete probability spectrum consisting of only 20 significant spectral
lines. A simple method for estimating the informativity of each of the
important spectral components is proposed. Traditional statistical assessments
can be strengthened by the following deeper level of the spectral components
analysis of small samples of biometric data. The second deeper level of
statistical processing should be substantially more powerful. Under the same
conditions, the computational informativity increases from 2.22 bits to 24.95
bits due to the transition from simple continual calculations to discrete
calculations of high computational complexity.
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